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<p>Desloratadine (DL) and montelukast sodium (MTS) are combined in a pharmaceutical preparation for the treatment of allergic rhinitis and asthma. DL is a competitive H1-receptor antagonist and helps the management of allergic reactions and relieves allergy symptoms, while MTS is a leukotriene receptor antagonist that inhibits the effects of inflammatory mediators. This study introduces a machine learning-assisted UV-Vis spectrophotometric method for the simultaneous quantification of DL and MTS in a commercial film-coated tablet, addressing limitations due to spectral overlap.</p><p>Five machine learning regression models (Support Vector Regression, Ridge Regression, Lasso Regression, Elastic Net, and Linear Regression) were evaluated. Ridge Regression (λ = 0.1) was selected for its balance of accuracy, computational efficiency, and robustness. The method was applied using Aircomb<sup>®</sup> film-coated tablets, containing 5&#xa0;mg DL and 10.4&#xa0;mg MTS, ensuring high precision.</p><p>The developed method demonstrated high recovery rates (99.25% for DL and 101.0% for MTS with minimal relative standard deviation (≤ 1.59%). Sustainability assessments using Analytical GREEnness Metric (AGREE) and Complex Green Analytical Procedure Index (ComplexGAPI) confirmed its alignment with green analytical chemistry principles. Ridge Regression (λ = 0.1) provided accurate and reproducible results, making it suitable for routine pharmaceutical analysis.</p><p>This study highlights the potential of machine learning-assisted UV-Vis spectrophotometry as a cost-effective and environmentally friendly alternative for pharmaceutical quality control. The method minimizes solvent consumption while ensuring analytical precision. Future research may explore non-linear models, such as artificial neural networks, to enhance predictive performance and broaden its applicability.</p>

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Bioinformatics and machine learning-based simultaneous spectrophotometric identification of Montelukast sodium and Desloratadine in a commercial film-coated tablet

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Fatma Demirkaya Miloglu,
  • Burak Bayrak,
  • Hira Beril Kucuk,
  • Ibrahim Yucel Ozbek,
  • Meltem Cetin

摘要

Desloratadine (DL) and montelukast sodium (MTS) are combined in a pharmaceutical preparation for the treatment of allergic rhinitis and asthma. DL is a competitive H1-receptor antagonist and helps the management of allergic reactions and relieves allergy symptoms, while MTS is a leukotriene receptor antagonist that inhibits the effects of inflammatory mediators. This study introduces a machine learning-assisted UV-Vis spectrophotometric method for the simultaneous quantification of DL and MTS in a commercial film-coated tablet, addressing limitations due to spectral overlap.

Five machine learning regression models (Support Vector Regression, Ridge Regression, Lasso Regression, Elastic Net, and Linear Regression) were evaluated. Ridge Regression (λ = 0.1) was selected for its balance of accuracy, computational efficiency, and robustness. The method was applied using Aircomb® film-coated tablets, containing 5 mg DL and 10.4 mg MTS, ensuring high precision.

The developed method demonstrated high recovery rates (99.25% for DL and 101.0% for MTS with minimal relative standard deviation (≤ 1.59%). Sustainability assessments using Analytical GREEnness Metric (AGREE) and Complex Green Analytical Procedure Index (ComplexGAPI) confirmed its alignment with green analytical chemistry principles. Ridge Regression (λ = 0.1) provided accurate and reproducible results, making it suitable for routine pharmaceutical analysis.

This study highlights the potential of machine learning-assisted UV-Vis spectrophotometry as a cost-effective and environmentally friendly alternative for pharmaceutical quality control. The method minimizes solvent consumption while ensuring analytical precision. Future research may explore non-linear models, such as artificial neural networks, to enhance predictive performance and broaden its applicability.